David Menninger's Analyst Perspectives

Denodo Makes Data Virtualization Relevant to Big Data and Analytics

Data virtualization is not new, but it has changed over the years. The term describes a process of combining data on the fly from multiple sources rather than copying that data into a common repository such as a data warehouse or a data lake, which I have written about. There are many reasons for an organization concerned with managing its data to consider data virtualization, most stemming from the fact that the data does not have to be copied to a new location. It could, for instance, eliminate the cost of building and maintaining a copy of one of the organization’s big data sources. Recognizing these benefits, many database and data integration companies offer data virtualization products. Denodo, one of the few independent, best-of-breed vendors in this market today, brings these capabilities to big data sources and data lakes.

Google Trends presents a graphic representation of the decline of the popularity of the term data federation and the rise in popularity of the term data virtualization over time. The change in terminology corresponds with a change in technology. The industry has evolved from a data federation approach to today’s cost-based optimization approach. In a federated approach, queries are sent to the appropriate data sources without much intelligence about the overall query or the cost of the individual parts of the federated query. Each underlying data source performs its portion of the workload as best it can and returns the results. The various parts are combined and additional post-processing performed if necessary, for example to sort the combined result set.

Denodo takes a different approach. Its tools consider the costs of each part of the individual query and evaluate trade-offs. As the saying goes, there’s more than one way to skin a cat; in this case there’s more than one way to execute a SQL statement. For example, suppose you wish to create a list of all sales of a certain set of products. Your company has 1,000 products (maintained in one system) and hundreds of millions of customer transactions (maintained in another system). The federated approach would bring both data sets to the federated system, join them and then find the desired subset of products. An alternative would be to ship the table of 1,000 products to the system that holds the customer transactions, load it as a temporary table and join it to the customer transaction data to identify the desired subset before sending the product data back to its source. Today’s data virtualization evaluates the costs in time of the two alternatives and selects the one that would produce the result set the fastest.

Data virtualization can make it easier, and therefore faster, to set up access to data sources in an organization. Using Denodo users connect to existing data sources, which become available as a virtual resource. In the case of data warehouses or data lakes, this virtual representation is often referred to as a logical data warehouse or a logical data lake. No matter how hard you work to consolidate data into a central repository, there are often pieces of data that have to be combined from multiple data sources. We find that such issues are common. In our big data integration benchmark research one-fourth (26%) of organizations said that data virtualization is a key activity for their big data analytics, yet only 14 percent said that they have adequate data virtualization capabilities.

Not all the work is eliminated by data virtualization. You must still design the logical model for the data that you want to provide, such as which tables and which columns to include, but that’s all. Virtualization eliminates load processes and the need to update the data. In the case of big data, there are no extra clusters to set up and maintain. The logical data warehouse or data lake uses the security and governance system already in place. As a result, users can avoid some of the organizational battles about data access since the “owner” of the data continues to maintain the rights and restrictions on the data. Our research shows that organizations that have adequate data virtualization capabilities are more often satisfied with the way their organization manages big data than are organizations as a whole (88% vs. 58%) and are more confident in the data quality of their big data integration efforts (81% vs. 54%).

In its most recent release, version 6.0, Denodo enhanced its cost-based query optimizer for data virtualization. Many of the optimizer’s features would be found in any decent relational database management system, but the challenge becomes greater when the underlying resources are scattered among multiple systems. To address this issue Denodo collects and maintains statistics about the various data sources that are evaluated at run time to determine the optimal way to execute queries. The product offers connectivity to a variety of data sources, both structured and unstructured, including Hadoop, NoSQL, documents and websites. It can be deployed on premises, in the cloud using Amazon Web Services or in a hybrid configuration.

Performance can be a key factor in user acceptance of data virtualization; users will balk if access is too slow. Denodo has published some benchmarks showing that performance of its product can be nearly identical to accessing data loaded into an analytical database. I never place much emphasis on vendor benchmarks as they may or may not reflect an actual organization’s configuration and requirements. However, the fact that Denodo produces this type of benchmark indicates its focus on minimizing the performance overhead associated with data virtualization.

When I first looked at Denodo, prior to the 6.0 release, I expected to see more optimization techniques built into the product. There’s always room for improvement, but with the current release the company has made great strides and addressed many of these issues. In order to maximize the software’s value to customers, I’d like to see the company invest in developing more technology partnerships with providers of data sources and analytic tools. Users would also find it valuable if Denodo could help manage and present consolidated lineage information. Not only do users need access to data, they need to understand how data is transformed both inside and outside Denodo.

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David is responsible for the overall research direction of data, information and analytics technologies at Ventana Research covering major areas including Analytics, Big Data, Business Intelligence and Information Management along with the additional specific research categories including Information Applications, IT Performance Management, Location Intelligence, Operational Intelligence and IoT, and Data Science. David is also responsible for examining the role of cloud computing, collaboration and mobile technologies as they affect these areas. David brings to Ventana Research over twenty-five years of experience, through which he has marketed and brought to market some of the leading edge technologies for helping organizations analyze data to support a range of action-taking and decision-making processes. Prior to joining Ventana Research, David was the Head of Business Development & Strategy at Pivotal a division of EMC, VP of Marketing and Product Management at Vertica Systems, VP of Marketing and Product Management at Oracle, Applix, InforSense and IRI Software. David earned his MS in Business from Bentley University and a BS in Economics from University of Pennsylvania.

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